Smart Construction and Sustainable Cities
2731-9032
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A stacked generalisation methodology for estimating the uniaxial compressive strength of rocksAbstract
Tập 1 Số 1
Uniaxial compressive strength (UCS) has become a highly essential strength parameter in the mining, civil and geomechanical industries. Estimating the exact value of the strength of rock has become a matter of great concern in real life. Despite this, there have been many works to indirectly/directly estimate the UCS of rocks. This study introduces a novel stacked generalisation methodology for estimating the UCS of rocks in geomechanics. In this study, generalised regression neural network (GRNN), radial basis function neural network (RBFNN), and random forest regression (RF) were used as the base learners and the multivariate adaptive regression spline (MARS) functioned as the meta-learner for the proposed stacking method. The proposed 3-Base learner stack model exhibited dominance over single applied AI methods of GRNN, RBFNN, and RF when confirmed with similar datasets by employing performance metrics like the Nash–Sutcliffe Efficiency Index (
Hiệu suất theo dõi bánh xe của cốt liệu bê tông tái chế với kính và gạch tái chế trong các mặt đường không có kết cấu dưới tải trọng cao Dịch bởi AI Tóm tắt
Tập 1 Số 1
Khi các vật liệu khai thác tự nhiên ngày càng hiếm và kinh tế, ngành xây dựng đã chuyển sang các lựa chọn bền vững như chất thải xây dựng và phá dỡ (C&D) và kính tái chế cho xây dựng đường. Mục tiêu của nghiên cứu này là đánh giá hiệu suất của các hỗn hợp bao gồm kính tái chế (RG), gạch nghiền (CB) và cốt liệu bê tông tái chế (RCA) dưới các điều kiện giao thông khác nhau. Việc đánh giá này đã được thực hiện thông qua các thử nghiệm theo dõi bánh xe (WT) dưới các điều kiện giao thông cao giả lập, trong đó các hỗn hợp phải chịu tải trọng dọc tăng cao và số vòng quay tải trọng nhiều hơn so với các nghiên cứu trước đó. Nghiên cứu cho thấy cả các hỗn hợp RCA + 20%RG và RCA + 20%CB đều hiển thị độ biến dạng bề mặt trung bình tương đương hoặc hơi lớn hơn so với đá nghiền tự nhiên dưới các điều kiện mặc định. Các điều kiện mặc định do cơ quan quản lý đường địa phương quy định bao gồm tải trọng bánh xe 8 kN và 40.000 chu kỳ tải. Nghiên cứu cũng cho thấy cả hai hỗn hợp đều có sự gia tăng nhất quán về độ sâu lún khi số chu kỳ tăng lên tới 100.000 trong khi chịu tải trọng bánh xe 20 kN. Độ sâu lún tối đa của RCA + 20%RG gần như ở giới hạn thấp nhất của phạm vi độ sâu lún tối đa cho phép được quy định bởi các cơ quan quản lý đường. Điều này gợi ý rằng các hỗn hợp này đang ở giới hạn của khả năng chịu tải trọng nặng trên các con đường có mật độ giao thông cao.
Machine learning approach for predicting compressive strength in foam concrete under varying mix designs and curing periods
Tập 1 - Trang 1-17 - 2023
Efforts to reduce the weight of buildings and structures, counteract the seismic threat to human life, and cut down on construction expenses are widespread. A strategy employed to address these challenges involves the adoption of foam concrete. Unlike traditional concrete, foam concrete maintains the standard concrete composition but excludes coarse aggregates, substituting them with a foam agent. This alteration serves a dual purpose: diminishing the concrete’s overall weight, thereby achieving a lower density than regular concrete, and creating voids within the material due to the foam agent, resulting in excellent thermal conductivity. This article delves into the presentation of statistical models utilizing three different methods—linear (LR), non-linear (NLR), and artificial neural network (ANN)—to predict the compressive strength of foam concrete. These models are formulated based on a dataset of 97 sets of experimental data sourced from prior research endeavors. A comparative evaluation of the outcomes is subsequently conducted, leveraging statistical benchmarks like the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), with the aim of identifying the most proficient model. The results underscore the remarkable effectiveness of the ANN model. This is evident in the ANN model’s R2 value, which surpasses that of the LR model by 36% and the non-linear model by 22%. Furthermore, the ANN model demonstrates significantly lower MAE and RMSE values compared to both the LR and NLR models.
Sustainable construction and quality of improved columns with three types of water-cement ratios on deep mixing method in Saga Lowland, Kyushu, Japan
- 2024
In this study, we investigated the application of the deep mixing method (DMM) to cohesive soil in the Saga Lowland of Kyushu, Japan. The study focused on examining three types of water-cement ratio (W/C) conditions, with a constant addition of cement-based binder (C). Due to the soft clay nature of the Saga Lowland, frequent ground settlement and deformation occur, necessitating measures to prevent adverse effects on the surrounding environment. The objective of this research is to provide a valuable approach to optimizing the quality of the improved columns while minimizing ground displacement in an environmentally considerate manner. In Saga Lowland, it is common to fix the W/C ratio at 1.0 and vary the cement content. Through experimental construction for improved columns on the field, the study confirmed that W/C values of 0.5, 1.0, and 1.5 influence the quality of the improved structure. A higher W/C value of 1.5 resulted in a more fluid cement slurry due to a higher injection rate (IR = 23.9%), as evidenced by statistical analysis revealing higher average unconfined compressive strength (
$$\overline{{q }_{u}}$$
), and a lower coefficient of variation (CV). The defective rate of 10% (qudr) from the design standard strength shows that values are lowest for Case 2, followed by Case 3 and then Case 1. Comparing the values of Case 2 and Case 3, it is observed that in Case 3, with a higher W/C, the CV is lower.. Regarding horizontal ground displacement (Sh), Case 3 exhibited a Sh value of 2.0 to 6.5 mm, significantly lower than Saga prefecture standards (20 mm). This outcome is attributed to reduced viscosity during mixing, leading to improved fluidity and minimal lateral displacement of the soil–cement columns (which often results in lateral ground uplift). Even though with a higher W/C = 1.5, the implementation cost remains the same, but the constructed structure would be of higher quality and smaller displacement, with the overall structure corresponding to the standard quality. The study includes the specific geotechnical conditions of the Saga Lowland and the scope of experimentation. Nonetheless, in terms of the applicability and optimization of DMM in Saga Lowland, the findings provide practical guidance for engineers in selecting W/C ratio and IR during construction for future DMM implementations, thereby contributing to the development of long-lasting infrastructure and sustainable societal development.
Intelligent compaction methods and quality control
Tập 1 - Trang 1-22 - 2023
Ensuring high-quality fill compaction is crucial for the stability and longevity of infrastructures and affects the sustainability of urban infrastructure networks. The purpose of this paper is to provide a refined analysis and insight understanding of the current practice, limitations, challenges, and future development trends of compaction methods from the perspective of the development stage. This paper offers a comprehensive overview of the evolution of compaction methods and classifies compaction quality control methods into four groups through quantitative analysis of literature: traditional compaction methods, digital compaction methods, automated compaction methods, and intelligent compaction methods. Each method's properties and issues are succinctly stated. Then, the research on three key issues in intelligent compaction including compaction quality evaluation algorithms, dynamic optimal path planning, and implementation of unmanned technology is summarized. Currently, the field of intelligent compaction is far from mature, a few challenges and limitations need further investigation: coupling problems of multiple indicators in intelligent evaluation algorithms, unmanned roller groups collaborative control problems, and intelligent decision-making and optimization problems of multi-vehicle compaction paths. This review serves as a valuable reference for systematically understanding the development of compaction methods.
Quantitative characterization method of 3D roughness of rock mass structural surface considering size effect
Tập 1 - Trang 1-12 - 2023
The surface morphology of the structural surface of the rock mass plays a crucial role in determining its macroscopic physical and mechanical properties, including shear strength and seepage characteristics. The morphological characteristics of the rock mass structure exhibit significant anisotropy and size effects. The distribution characteristics of the two key indicators that affect the morphological characteristics of the structure were analyzed, revealing that the undulation degree and undulation angle conform to the normal distribution and Weibull distribution, respectively. The present study defines a method for quantifying the 3D roughness of structural surface based on the features of undulation degree and undulation angle. Through quantitative analysis, it was observed that the roughness parameters exhibit anisotropic characteristics at different sampling intervals and shear directions.
Prevention/mitigation of natural disasters in urban areas
Tập 1 Số 1 - Trang 1-16 - 2023
Preventing/mitigating natural disasters in urban areas can indirectly be part of the 17 sustainable economic and social development intentions according to the United Nations in 2015. Four types of natural disasters—flooding, heavy rain-induced slope failures/landslides; earthquakes causing structure failure/collapse, and land subsidence—are briefly considered in this article. With the increased frequency of climate change-induced extreme weathers, the numbers of flooding and heavy rain-induced slope failures/landslides in urban areas has increased in recent years. There are both engineering methods to prevent their occurrence, and more effectively early prediction and warning systems to mitigate the resulting damage. However, earthquakes still cannot be predicted to an extent that is sufficient to avoid damage, and developing and adopting structures that are resilient against earthquakes, that is, structures featuring earthquake resistance, vibration damping, and seismic isolation, are essential tasks for sustainable city development. Land subsidence results from human activity, and is mainly due to excessive pumping of groundwater, which is a “natural” disaster caused by human activity. Countermeasures include effective regional and/or national freshwater management and local water recycling to avoid excessive pumping the groundwater. Finally, perspectives for risk warning and hazard prevention through enhanced field monitoring, risk assessment with multi-criteria decision-making (MCDM), and artificial intelligence (AI) technology.
Multi-tier scheduling algorithm of dispatching systems for urban water logging
- 2024
Due to global warming, considerable amounts of storm rain have occurred, causing urban water logging and flooding. The efficient scheduling of drainage systems among pumping stations is crucial to mitigating flash flooding in urban areas. This study introduces a Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm as a proactive solution for addressing urban flooding through the optimization of drainage system discharge capacities. The algorithm's robustness is guaranteed through the integration of a multi-tier drainage system and dependency relationships. Additionally, the incorporation of an importance parameter is considered for facilitating the practical exploration of flooding risk evaluation. The proposed model was applied to simulate a drainage system in Haining City, and the results indicate that its accuracy, flexibility and reliability outperform that of existing algorithms such as fixed-priority scheduling. Moreover, the proposed approach enabled a considerable reduction in overflow loss and improved the efficiency of the sewage system. This method can improve the responses of cities to the rising problem of urban water logging.
Building segmentation from UAV orthomosaics using unet-resnet-34 optimised with grey wolf optimisation algorithm
Tập 1 - Trang 1-18 - 2023
Given the importance and interest of buildings in the urban environment, numerous studies have focused on automatically extracting building outlines by exploiting different datasets and techniques. Recent advancements in unmanned aerial vehicles (UAVs) and their associated sensors have made it possible to obtain high-resolution data to update building information. These detailed, up-to-date geographic data on the built environment are essential and present a practical approach to comprehending how assets and people are exposed to hazards. This paper presents an effective method for extracting building outlines from UAV-derived orthomosaics using a semantic segmentation approach based on a U-Net architecture with a ResNet-34 backbone (UResNet-34). The novelty of this work lies in integrating a grey wolf optimiser (GWO) to fine-tune the hyperparameters of the UResNet-34 model, significantly enhancing building extraction accuracy across various localities. The experimental results, based on testing data from four different localities, demonstrate the robustness and generalisability of the approach. In this study, Locality-1 is well-laid buildings with roads, Locality-2 is dominated by slum buildings in proximity, Locality-3 has few buildings with background vegetation and Locality-4 is a conglomeration of Locality-1 and Locality-2. The proposed GWO-UResNet-34 model produced superior performance, surpassing the U-Net and UResNet-34. Thus, for Locality-1, the GWO-UResNet-34 achieved 94.74% accuracy, 98.11% precision, 84.85% recall, 91.00% F1-score, and 88.16% MIoU. For Locality-2, 90.88% accuracy, 73.23% precision, 75.65% recall, 74.42% F1-score, and 74.06% MioU was obtained.The GWO-UResNet-34 had 99.37% accuracy, 90.97% precision, 88.42% recall, 89.68% F1-score, and 90.21% MIoU for Locality-3, and 95.30% accuracy, 93.03% precision, 89.75% recall, 91.36% F1-score, and 88.92% MIoU for Locality-4.